Comparative studies on the excitation control of a doubly fed induction generator using fuzzy PI controllers and classical PI/PID controllers
DOI:
https://doi.org/10.15837/ijccc.2025.6.6887Keywords:
Fuzzy PI controller, doubly fed induction generator, conventional PI/PID control system, wind energy conversion systemAbstract
The main goal of this paper is to answer the question whether a fuzzy PI control can provide better performance than a conventional PI/PID control in the case of a wind energy conversion system with a Doubly-Fed Induction Generator (DFIG) connected to a power system. The paper emphasizes the main advantage of fuzzy PI controllers: their ability to produce a non-zero control increment even when the output error is zero, but its derivative is not, proving a better performance than a conventional PI/PID solution for the control of high-order and strongly oscillating processes (with an error repeatedly crossing zero). This remark, according to the authors’ knowledge, has not been encountered in the specialized literature, being valid also for the fuzzy control of other strongly oscillating processes. To prove the above, the research presents a comparative study on the excitation control of a DFIG, using fuzzy PI controllers, respectively conventional PI/PID controllers. The goal of the control system is to keep constant the generator terminal voltage under some external disturbances action such as variations of the mechanical torque (due to wind speed changes on the wind turbine) and electrical load/unload (by connecting/disconnecting local consumers). Based on comparative analysis of type-1 and type-2 fuzzy PI control systems, it was concluded that, for the considered process (DFIG), type-2 fuzzy controller does not significantly improve the control performance. Therefore, the type-2 fuzzy controller is not justified, being even more complex and difficult to tune. The comparative study carried out between the fuzzy PI control solution (type-1) and the classical solutions using PI/PID controllers shows that the fuzzy strategy provides superior performance (smaller settling-time, shorter duration oscillations), both in case of a slow mechanical disturbance (mechanical torque variation), as well as for fast electrical disturbances (load/unload). It should also be highlighted that the DFIG is modelled as a nonlinear system of the 7th order (using Park’s classical d-q equations) ensuring an increased accuracy for the obtained results in the context of fast transient regimes specific to the considered electrical process
References
Alcantara, S.; Vilanova, R.; Pedret, C.; Skogestad, S. (2012). A look into robustness/performance and servo/regulation issues in PI tuning, IFAC Proceedings Volumes, 45(3), 181-186, 2012. https://doi.org/10.3182/20120328-3-IT-3014.00031
Anbalagan, P.; Joo, Y. H. (2024). Nonfragile Sampled-Data Control for Interval Type-2 Fuzzy Modeling of Permanent Magnet Synchronous Generator-Based Wind Turbine Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(4), 2426-2439, 2024. https://doi.org/10.1109/TSMC.2023.3344111
Aoun, S; Boukadoum, A; Yousfi, L. (2023). Advanced power control of a variable speed wind turbine based on a doubly fed induction generator using field-oriented control with fuzzy and neu ral controllers, International Journal of Dynamics and Control, 2023. https://doi.org/10.1007/s40435-023-01345-9
Arifin, M. S.; Uddin, M. N.; Wang, W. (2023). Neuro-Fuzzy Adaptive Direct Torque and Flux Control of a Grid-Connected DFIG-WECS With Improved Dynamic Performance, IEEE Transactions on Industry Applications, 59(6), 7692-7700, 2023. https://doi.org/10.1109/TIA.2023.3302844
Azadegan, A.; Porobic, L.; Ghazinoory, S.; Samouei, P.; Kheirkhah, A.S. (2011). Fuzzy logic in manufacturing: A review of literature and a specialized application, International Journal of Production Economics, 132, 258-270, 2011. https://doi.org/10.1016/j.ijpe.2011.04.018
Bensaadia, L.R.; Rouabhi, R.; Khodja, D.; Herizi, A. (2023). Adaptive type-1 fuzzy control of a wind energy conversion system based on a double-fed induction machine, PRZEGLĄD ELEKTROTECHNICZNY, 99. 110-115. https://doi.org/10.15199/48.2023.08.19
Bustan, D.; Moodi, H. (2022). Adaptive Interval Type-2 Fuzzy Controller for Variablespeed Wind Turbine, Journal of Modern Power Systems and Clean Energy, 10(2), 524-530, 2022. https://doi.org/10.35833/MPCE.2019.000374
Cheng, X. (2023). A Fuzzy Adaptive PID Control Method for Novel Designed Rail Grinding Equipment, IEEE Access, 11, 118-124, 2023. https://doi.org/10.1109/ACCESS.2022.3232578
Elnaghi, B. E.; Abelwhab, M. N.; Abdel-Kader, F. E. S. A.; Ismaiel, A. M.; Mohammed, R. H.; Dessouki, M. E. (2023). Experimental Validation of Second-Order Adaptive Fuzzy Logic Controller for Grid-Connected DFIG Wind Power Plant, IEEE Access, 11, 135255-135271, 2023. https://doi.org/10.1109/ACCESS.2023.3337829
Fekry, H. M.; Eldesouky, A. A.; Kassem, A. M.; Abdelaziz, A. Y. (2020). Power Management Strategy Based on Adaptive Neuro Fuzzy Inference System for AC Microgrid, IEEE Access, 8, 192087-192100, 2020. https://doi.org/10.1109/ACCESS.2020.3032705
Filip, I.; Dragan, F.; Szeidert, I. Albu, A (2020). Minimum-Variance Control System with Variable Control Penalty Factor, APPLIED SCIENCES, 10(17), 2020. https://doi.org/10.3390/app10072274
Filip, I.; Dragan, F.; Szeidert, I. (2021). Considerations about Parameters Estimation into a Minimum Variance Control System, APPLIED SCIENCES, 11(13), 2021. https://doi.org/10.3390/app11136165
Filip, I.; Mihet-Popa, L.; Vasar, C.; Prostean, O.; Szeidert, I. (2019). Considerations Regarding the Design of a Minimum Variance Control System for an Induction Generator, Electronics, 8(5), 2019. https://doi.org/10.3390/electronics8050532
Filip, I.; Szeidert, I. (2017). Tuning the control penalty factor of a minimum variance adaptive controller, EUROPEAN JOURNAL OF CONTROL, 37, 16-26, 2017. https://doi.org/10.1016/j.ejcon.2017.04.005
Filip, I.; Szeidert, I.; Prostean O. (2016). Mathematical Modelling and Numerical Simulation of the Dual Winded Induction Generator's Operating Regimes, 6th International Workshop on Soft Computing Applications (SOFA), 2(357), 1161-1170, 2016. https://doi.org/10.1007/978-3-319-18416-6_94
Filip, I.; Szeidert, I.; Prostean, O.; Vasar, C. (2013). Analysis through Simulation of a Self-Tuning Control Structure for Dual Winded Induction Generator, IEEE 9th International Conference on Computational Cybernetics, JUL 08-10, 2013, Tihany, Hungary,147-152, 2013. https://doi.org/10.1109/ICCCyb.2013.6617578
Filip, I.; Szeidert, I. (2016). Adaptive fuzzy PI controller with shifted control singletons, Expert Systems With Applications, 54, 1-12, 2016. https://doi.org/10.1016/j.eswa.2016.01.036
Filip, I.; Vasar, C.; Szeidert, I.; Prostean, O. (2019). Self-tuning strategy for a minimum variance control system of a highly disturbed process, EUROPEAN JOURNAL OF CONTROL, 46, 49-62, 2019. https://doi.org/10.1016/j.ejcon.2018.06.004
Haro, A.; Young, H.; Pavez, B. (2021). Fuzzy Logic Active Yaw Control of a Low-Power Wind Generator, IEEE Latin America Transactions, 19(11), 1941- 1948, 2021. https://doi.org/10.1109/TLA.2021.9475848
Kaddache, M.; Drid, S.; Khemis, A.; Rahem, D.; Chrifi-Alaoui, L. (2024). Maximum power point tracking improvement using type-2 fuzzy controller for wind system based on the double fed induction generator, Electrical Engineering & Electromechanics, 2, 2024. https://doi.org/10.20998/2074-272X.2024.2.09
Lu, J.; Guangzhi, M.; Guangquan, Z. (2024). Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review, IEEE Transactions on Fuzzy Systems, 32(7), 3861-3878, 2024. https://doi.org/10.1109/TFUZZ.2024.3387429
Mittal, K.; Jain A.; Vaisla, K. S.; Castillo O.; Kacprzyk, J. (2020). A comprehensive review on type 2 fuzzy logic applications: Past, present and future, Engineering Applications of Artificial Intelligence, 95, 2020. https://doi.org/10.1016/j.engappai.2020.103916
Nguyen, T. -T.; Nguyen, D. -M.; Ngo, Q. -V. (2021). The Power-Sharing System of DFIGBased Shaft Generator Connected to a Grid of the Ship, IEEE Access, 9, 109785-109792, 2021. https://doi.org/10.1109/ACCESS.2021.3102659
Pillutla, H.; Arjunan, A. (2018). A Brief Review of Fuzzy Logic and Its Usage Towards Counter- Security Issues, International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India, 22-24 March 2018. https://doi.org/10.1109/WiSPNET.2018.8538555
Puchalapalli, S.; Singh, B. (2020). Viewpoint research evaluation for computer science, IEEE Transactions on Sustainable Energy, 11(2),595-607, 2020. https://doi.org/10.1109/TSTE.2019.2898115
Rouabhi, R; Herizi, A; Djeriou,; Zemmit, A. (2024). Hybrid Type-1 and 2 fuzzy sliding mode control of the induction motor, Revue Roumaine des Sciences Techniques-Serie Electrotechnique et Energetique, 69 (2), 2024. https://doi.org/10.59277/RRST-EE.2024.2.5
Shanmugam, L.; Joo, Y. H. (2021). Stability and Stabilization for T-S Fuzzy Large-Scale Interconnected Power System With Wind Farm via Sampled-Data Control, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2134-2144, 2021. https://doi.org/10.1109/TSMC.2020.2965577
Sharmila, V.; Rakkiyappan, R.; Joo, Y. H. (2021). Fuzzy Sampled-Data Control for DFIG-Based Wind Turbine With Stochastic Actuator Failures, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2199-2211, 2021. https://doi.org/10.1109/TSMC.2019.2946873
Soliman, M. A.; Hasanien, H. M.; Azazi, H. Z.; El-Kholy, E. E. ; Mahmoud, S. A. (2019). An Adaptive Fuzzy Logic Control Strategy for Performance Enhancement of a Grid- Connected PMSG-Based Wind Turbine, IEEE Transactions on Industrial Informatics, 15(6), 3163-3173, 2019. https://doi.org/10.1109/TII.2018.2875922
Ullah, N.; Sami, I.; Chowdhury, M. S.; Techato, K.; Alkhammash, H. I. (2020). Artificial Intelligence Integrated Fractional Order Control of Doubly Fed Induction Generator-Based Wind Energy System, IEEE Access, 9, 5734-5748, 2021. https://doi.org/10.1109/ACCESS.2020.3048420
Vilanova, R.; Alfaro, V.M.; Arrieta, O. (2012). Robustness in PID Control. In: Vilanova, R., Visioli, A. (eds) PID Control in the Third Millennium. Advances in Industrial Control. Springer, London, 113-145, 2012. https://doi.org/10.1007/978-1-4471-2425-2_4
Xu, M.; Jin, Y.; Ma, J.; Wang, C.; Liu, P. (2023). Fuzzy Frequency Droop Control of DFIG Wind Turbine Generators Adapted to Continuous Changes in Wind Speeds, IEEE Access, 11, 115011-115024, 2023. https://doi.org/10.1109/ACCESS.2023.3325245
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Iosif Szeidert, Ioan Filip, Cristian Vasar, Dorin Bordeasu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
